Learning Localized Spatio-Temporal Models From Streaming Data

TL;DR

This paper introduces a localized spatio-temporal covariance model for streaming data that captures spatially varying temporal patterns, enabling accurate real-time predictions in climate and synthetic datasets.

Contribution

It presents a novel covariance-fitting approach for sequentially updating spatio-temporal models with streaming data, addressing spatial heterogeneity in temporal patterns.

Findings

Accurately predicts missing data in spatial regions over time.

Demonstrates effectiveness on synthetic and real climate datasets.

Abstract

We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the training dataset is augmented sequentially. Specifically, we develop a localized spatio-temporal covariance model of the process that can capture spatially varying temporal periodicities in the data. We then apply a covariance-fitting methodology to learn the model parameters which yields a predictor that can be updated sequentially with each new data point. The proposed method is evaluated using both synthetic and real climate data which demonstrate its ability to accurately predict data missing in spatial regions over time.